def visualization(y, x, mean_x, std_x, w, save_name, is_LR=False):
"""visualize the raw data as well as the classification result."""
fig = plt.figure()
# plot raw data
x = de_standardize(x, mean_x, std_x)
ax1 = fig.add_subplot(1, 2, 1)
males = np.where(y == 0)
females = np.where(y == 1)
ax1.scatter(
x[males, 0],
x[males, 1],
marker=".",
color=[1, 0.06, 0.06],
s=20,
label="male sample",
)
ax1.scatter(
x[females, 0],
x[females, 1],
marker="*",
color=[0.06, 0.06, 1],
s=20,
label="female sample",
)
ax1.set_xlabel("Height")
ax1.set_ylabel("Weight")
ax1.legend()
ax1.grid()
# plot raw data with decision boundary
ax2 = fig.add_subplot(1, 2, 2)
height = np.arange(np.min(x[:, 0]), np.max(x[:, 0]) + 0.01, step=0.01)
weight = np.arange(np.min(x[:, 1]), np.max(x[:, 1]) + 1, step=1)
hx, hy = np.meshgrid(height, weight)
hxy = (np.c_[hx.reshape(-1), hy.reshape(-1)] - mean_x) / std_x
x_temp = np.c_[np.ones((hxy.shape[0], 1)), hxy]
# The threshold should be different for least squares and logistic regression when label is {0,1}.
# least square: decision boundary t >< 0.5
# logistic regression: decision boundary sigmoid(t) >< 0.5 <==> t >< 0
if is_LR:
prediction = x_temp.dot(w) > 0.0
else:
prediction = x_temp.dot(w) > 0.5
prediction = prediction.reshape((weight.shape[0], height.shape[0]))
cs = ax2.contourf(hx, hy, prediction, 1)
proxy = [
plt.Rectangle((0, 0), 1, 1, fc=pc.get_facecolor()[0]) for pc in cs.collections
]
ax2.legend(proxy, ["prediction male", "prediction female"])
ax2.scatter(x[females, 0], x[females, 1], marker="*", color=[0.06, 0.06, 1], s=20)
ax2.scatter(x[males, 0], x[males, 1], marker=".", color=[1, 0.06, 0.06], s=20)
ax2.set_xlabel("Height")
ax2.set_ylabel("Weight")
ax2.set_xlim([min(x[:, 0]), max(x[:, 0])])
ax2.set_ylim([min(x[:, 1]), max(x[:, 1])])
plt.tight_layout()
plt.savefig(save_name + ".png")
def visualization(y, x, mean_x, std_x, w, save_name):
"""visualize the raw data as well as the classification result."""
fig = plt.figure()
# plot raw data
x = de_standardize(x, mean_x, std_x)
ax1 = fig.add_subplot(1, 2, 1)
males = np.where(y == 1)
females = np.where(y == 0)
ax1.scatter(x[males, 0],
x[males, 1],
marker='.',
color=[0.06, 0.06, 1],
s=20)
ax1.scatter(x[females, 0],
x[females, 1],
marker='*',
color=[1, 0.06, 0.06],
s=20)
ax1.set_xlabel("Height")
ax1.set_ylabel("Weight")
ax1.grid()
# plot raw data with decision boundary
ax2 = fig.add_subplot(1, 2, 2)
height = np.arange(np.min(x[:, 0]), np.max(x[:, 0]) + 0.01, step=0.01)
weight = np.arange(np.min(x[:, 1]), np.max(x[:, 1]) + 1, step=1)
hx, hy = np.meshgrid(height, weight)
hxy = (np.c_[hx.reshape(-1), hy.reshape(-1)] - mean_x) / std_x
x_temp = np.c_[np.ones((hxy.shape[0], 1)), hxy]
prediction = x_temp.dot(w) > 0.5
prediction = prediction.reshape((weight.shape[0], height.shape[0]))
ax2.contourf(hx, hy, prediction, 1)
ax2.scatter(x[males, 0],
x[males, 1],
marker='.',
color=[0.06, 0.06, 1],
s=20)
ax2.scatter(x[females, 0],
x[females, 1],
marker='*',
color=[1, 0.06, 0.06],
s=20)
ax2.set_xlabel("Height")
ax2.set_ylabel("Weight")
ax2.set_xlim([min(x[:, 0]), max(x[:, 0])])
ax2.set_ylim([min(x[:, 1]), max(x[:, 1])])
plt.tight_layout()
plt.savefig(save_name)
def visualization(y, x, mean_x, std_x, w, save_name):
"""visualize the raw data as well as the classification result."""
fig = plt.figure()
# plot raw data
x = de_standardize(x, mean_x, std_x)
ax1 = fig.add_subplot(1, 2, 1)
males = np.where(y == 1)
females = np.where(y == 0)
ax1.scatter(
x[males, 0], x[males, 1],
marker='.', color=[0.06, 0.06, 1], s=20)
ax1.scatter(
x[females, 0], x[females, 1],
marker='*', color=[1, 0.06, 0.06], s=20)
ax1.set_xlabel("Height")
ax1.set_ylabel("Weight")
ax1.grid()
# plot raw data with decision boundary
ax2 = fig.add_subplot(1, 2, 2)
height = np.arange(
np.min(x[:, 0]), np.max(x[:, 0]) + 0.01, step=0.01)
weight = np.arange(
np.min(x[:, 1]), np.max(x[:, 1]) + 1, step=1)
hx, hy = np.meshgrid(height, weight)
hxy = (np.c_[hx.reshape(-1), hy.reshape(-1)] - mean_x) / std_x
x_temp = np.c_[np.ones((hxy.shape[0], 1)), hxy]
prediction = x_temp.dot(w) > 0.5
prediction = prediction.reshape((weight.shape[0], height.shape[0]))
ax2.contourf(hx, hy, prediction, 1)
ax2.scatter(
x[males, 0], x[males, 1],
marker='.', color=[0.06, 0.06, 1], s=20)
ax2.scatter(
x[females, 0], x[females, 1],
marker='*', color=[1, 0.06, 0.06], s=20)
ax2.set_xlabel("Height")
ax2.set_ylabel("Weight")
ax2.set_xlim([min(x[:, 0]), max(x[:, 0])])
ax2.set_ylim([min(x[:, 1]), max(x[:, 1])])
plt.tight_layout()
plt.savefig(save_name)